Without a trace: Why did corona apps fail?. White, L. & Basshuysen, P. v. Journal of Medical Ethics, January, 2021.
Without a trace: Why did corona apps fail? [link]Paper  doi  abstract   bibtex   
At the beginning of the COVID-19 pandemic, high hopes were put on digital contact tracing, using mobile phone apps to record and immediately notify contacts when a user reports as infected. Such apps can now be downloaded in many countries, but as second waves of COVID-19 are raging, these apps are playing a less important role than anticipated. We argue that this is because most countries have opted for app configurations that cannot provide a means of rapidly informing users of likely infections while avoiding too many false positive reports. Mathematical modelling suggests that differently configured apps have the potential to do this. These require, however, that some pseudonymised data be stored on a central server, which privacy advocates have cautioned against. We contend that their influential arguments are subject to two fallacies. First, they have tended to one-sidedly focus on the risks that centralised data storage entails for privacy, while paying insufficient attention to the fact that inefficient contact tracing involves ethical risks too. Second, while the envisioned system does entail risks of breaches, such risks are also present in decentralised systems, which have been falsely presented as ‘privacy preserving by design’. When these points are understood, it becomes clear that we must rethink our approach to digital contact tracing in our fight against COVID-19.
@article{white_without_2021,
	title = {Without a trace: {Why} did corona apps fail?},
	copyright = {© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.. http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.},
	issn = {0306-6800, 1473-4257},
	shorttitle = {Without a trace},
	url = {https://jme.bmj.com/content/early/2021/01/08/medethics-2020-107061},
	doi = {10.1136/medethics-2020-107061},
	abstract = {At the beginning of the COVID-19 pandemic, high hopes were put on digital contact tracing, using mobile phone apps to record and immediately notify contacts when a user reports as infected. Such apps can now be downloaded in many countries, but as second waves of COVID-19 are raging, these apps are playing a less important role than anticipated. We argue that this is because most countries have opted for app configurations that cannot provide a means of rapidly informing users of likely infections while avoiding too many false positive reports. Mathematical modelling suggests that differently configured apps have the potential to do this. These require, however, that some pseudonymised data be stored on a central server, which privacy advocates have cautioned against. We contend that their influential arguments are subject to two fallacies. First, they have tended to one-sidedly focus on the risks that centralised data storage entails for privacy, while paying insufficient attention to the fact that inefficient contact tracing involves ethical risks too. Second, while the envisioned system does entail risks of breaches, such risks are also present in decentralised systems, which have been falsely presented as ‘privacy preserving by design’. When these points are understood, it becomes clear that we must rethink our approach to digital contact tracing in our fight against COVID-19.},
	language = {en},
	urldate = {2021-02-11},
	journal = {Journal of Medical Ethics},
	author = {White, Lucie and Basshuysen, Philippe van},
	month = jan,
	year = {2021},
	pmid = {33419939},
	keywords = {Articles, COVID-19, Multi-region, Surveillance \& Apps \& AI},
}

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